Predict Disease Progression Research Articles

Determinants of long‐term paramagnetic rim lesion evolution in people with multiple sclerosis

AbstractObjectiveBaseline paramagnetic rim lesion (PRL) load predicts disease progression in people with multiple sclerosis (pwMS). Understanding how PRLs relate to other known MS‐related factors, and the practical utility of PRLs in clinical trials, is crucial for informing clinical decision‐making and guiding development of novel disease‐modifying treatments (DMTs).MethodsThis study included 152 pwMS enrolled in a larger prospective, longitudinal cohort study who had 3T MRI scans and clinical assessments at baseline and 5‐ or 10‐year follow‐ups. PRLs were identified on baseline 3T quantitative susceptibility maps and classified as persisting, disappearing, or newly appearing at follow‐up. The relationships between PRL evolution and clinical, radiological, environmental, and genetic characteristics were assessed, and clinical trial sample sizes were estimated using PRL appearance or disappearance as outcome measures.ResultsDMT use was associated with lower odds of new PRL appearance (for high‐efficacy DMTs: odds ratio = 0.088, p = 0.024), but not disappearance. Current smoking status was associated with greater baseline PRL number (B = 0.527 additional PRLs, p = 0.013). A 24‐month clinical trial in people with progressive MS for a DMT that doubles the rate of PRL rim disappearance would require an estimated 118 people with progressive MS per group at 80% statistical power.InterpretationEarly MS diagnosis and subsequent DMT initiation may reduce new chronic active inflammation. However, the utility of PRL disappearance or new PRL appearance as outcome measures in clinical trials is limited by potentially large sample sizes that are needed for moderate efficacy drugs.

Differential functional change in olfactory bulb and olfactory eloquent areas in Parkinson’s disease

Abstract Olfactory dysfunction, or hyposmia, frequently occurs as a prodromal symptom and ongoing sign of Parkinson’s disease. Functional MRI is a powerful tool for studying functional changes in the olfactory brain regions in patients with Parkinson’s disease. However, existing studies show inconsistent results and no study has measured olfactory functional MRI abnormalities in the human olfactory bulb directly. This is mainly due to the well-known susceptibility artifacts in conventional functional MRI images that affect several key olfactory-eloquent brain regions, and especially the olfactory bulb. In this study, olfactory functional MRI was performed using a recently developed functional MRI approach that can minimize susceptibility artifacts and measure robust functional MRI signals in the human olfactory bulb during olfactory stimulation. Experiments were performed on high magnetic field (7T) in 24 early (< 5 years of parkinsonian symptoms) Parkinson’s disease patients and 31 matched healthy controls. Our data showed increased functional MRI signal changes (ΔS/S) in the olfactory bulb in patients with early Parkinson’s disease, which correlated with behavioral olfactory measures. Temporally, functional MRI signals in the olfactory bulb returned to the pre-stimulus state earlier after reaching peak amplitude in patients with early Parkinson’s disease, implicating a faster olfactory habituation effect. The piriform cortex showed reduced numbers of activated voxels in patients with early Parkinson’s disease, which correlated with behavioral olfactory assessment. Several secondary olfactory regions including the orbitofrontal cortex, temporal pole, and amygdala exhibited reduced numbers of activated voxels and increased functional MRI signal changes in patients with early Parkinson’s disease. Our data also showed that functional MRI results are highly dependent on voxel selection in the functional analysis. In summary, we demonstrate differential spatial and temporal characteristics of olfactory functional MRI signals between the primary and secondary olfactory regions in patients with early Parkinson’s disease. These results may assist the development of novel quantitative biomarkers (especially in the early stages of Parkinson’s disease) to track and predict disease progression, as well as potential treatment targets for early intervention.

The histological grading of fibrosis in Budd-Chiari syndrome: A chronic liver disease, different from others.

Budd-Chiari syndrome (BCS) is an uncommon disease caused by hepatic venous outflow obstruction. They can result in centrilobular fibrosis, nodular regenerative hyperplasia and cirrhosis. Assessing liver fibrosis is crucial for determining the stage of BCS, predicting disease progression and guiding treatment decisions. Although this pathology has been known for decades, no useful grading system was assigned. This study aims to introduce a histologic fibrosis grading system for BCS patients. All patients from 2017 to 2022 (Sanjay Gandhi Postgraduate Institute of Medical Sciences [SGPGIMS]), Lucknow diagnosed with BCS for whom liver biopsy was performed were included in the study. The Budd-Chiari syndrome-Hepatic Fibrosis system (BCS-HFS) was implemented to grade fibrosis. The fibrosis grade was compared with the fibrosis percentage area and a correlation was found with the hemodynamic variables (hepatic venous pressure gradient[HVPG])and the prognostic scores. There were 56 patients with BCS. The median age was 27years, with a male-female ratio of 1.8:1. There was a significant difference in the fibrosis percentage, hemorrhage percentage and model for end-stage liver disease (MELD) score among the BCS-HFS grades (p < 0.05). There was a significant correlation between BCS-HFS and HVPG (ρ = 0.699, p < 0.001) and the MELD prognostic score (ρ = 0.474, p < 0.001). BCS-HFS is applicable for grading fibrosis in BCS. It can help in uniform histopathology reporting and for further prospective and comparative studies.

Transforming Patient Outcomes: Cutting-Edge Applications of AI and ML in Predictive Healthcare

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in healthcare has emerged as a transformative force, offering unprecedented potential to enhance patient outcomes. This paper explores the cutting-edge applications of AI and ML in predictive healthcare, focusing on how these technologies are revolutionizing early detection, personalized treatment, and patient management. Through advanced algorithms and data-driven insights, AI and ML are enabling healthcare providers to predict disease progression, optimize therapeutic interventions, and improve patient monitoring in real-time. Key applications discussed include predictive modeling for chronic disease management, AI-powered diagnostic tools, and the use of machine learning in precision medicine. The paper also addresses the challenges associated with these technologies, including data privacy concerns, algorithmic bias, and the need for robust validation. By examining current case studies and future trends, this work aims to highlight the transformative impact of AI and ML on patient care, ultimately driving a shift toward more proactive, tailored, and efficient healthcare systems.

Molecular-Genetic Determinants of Chronic Heart Failure Associated with Type 2 Diabetes and Osteoporosis

This article presents the results of a study on the molecular-genetic factors influencing the development of chronic heart failure (CHF) comorbid with type 2 diabetes mellitus (T2DM) and osteoporosis in postmenopausal women. Polymorphic variants of the IL-1β, eNOS, and angiotensinogen genes were found to be associated with an increased risk of CHF in the presence of T2DM. Alleles and genotypes that can serve as predictors of disease progression were identified. Based on the obtained data, strategies for a personalized approach to the diagnosis and secondary prevention of CHF are proposed. The results emphasize the importance of genetic analysis for improving the prediction of cardiovascular complications and planning therapeutic measures.

DETECTION OF CLINICAL BIOMARKERS ASSOCIATED WITH HEPATO AND RENAL MANIFESTATIONS IN COVID-19 PATIENTS

The COVID-19 pandemic has spread in many countries worldwide, surpassing one million confirmed cases and resulting in deaths globally. Developing nations such as Pakistan face heightened vulnerability to such outbreaks due to limited healthcare infrastructure and resources. This study examines clinical biomarkers linked to hepatic and renal manifestations in COVID-19 patients in North Waziristan, recognizing the vulnerability of developing countries like Pakistan to such pandemics due to limited healthcare resources. Nasopharyngeal swabs were collected from 110 suspected COVID-19 patients visiting Dr. Azim Ullah clinic and DHQ Hospital North Waziristan, Miran Shah. Hematological, hepatic, renal, C-reactive protein (CRP), and ferritin markers were assessed. Most patients were male (75%), with varying age distributions. Abnormalities were observed in hematological parameters, liver enzymes, renal function markers, CRP, and ferritin levels. Significant correlations (p&lt;0.001) were found among these markers. The study concludes that inflammatory, hematological, renal, and liver markers are associated with COVID-19 infection, with higher levels indicating severe disease. Continuous monitoring of these biomarkers may enhance patient outcomes and aid in predicting disease progression

Cross-instrument optical coherence tomography-angiography (OCTA)-based prediction of age-related macular degeneration (AMD) disease activity using artificial intelligence

This study investigates the efficacy of predicting age-related macular degeneration (AMD) activity through deep neural networks (DNN) using a cross-instrument training dataset composed of Optical coherence tomography-angiography (OCTA) images from two different manufacturers. A retrospective cross-sectional study analyzed 2D vascular en-face OCTA images from Heidelberg Spectralis (1478 samples: 1102 training, 276 validation, 100 testing) and Optovue Solix (1003 samples: 754 training, 189 validation, 60 testing). OCTA scans were labeled based on clinical diagnoses and adjacent B-scan OCT fluid information, categorizing activity into normal, dry AMD, active wet AMD, and wet AMD in remission. Experiments explored cross-instrument disease classification using separate and combined datasets for training the DNN. Testing involved 100 Heidelberg and 60 Optovue samples. Training on Heidelberg data alone yielded 73% accuracy on Heidelberg images and 60% on Optovue images. Training on Optovue data alone resulted in 34% accuracy on Heidelberg and 85% on Optovue images. Combined training data from both instruments achieved 78% accuracy on Heidelberg and 76% on Optovue test sets. Results indicate that cross-instrument classifier training demonstrates high classification prediction accuracy, making cross-instrument training viable for future clinical applications. This implies that vascular morphology in OCTA can predict disease progression.

Physiopathological Aspects of Synovial Fluid and Membrane in Psoriatic Arthritis

Background: Psoriatic arthritis (PsA) is a chronic inflammatory arthropathy characterized by a variety of clinical manifestations, mainly affecting joints and entheses, but also skin, nails, the eye, and the intestine. Objectives: In this review, we describe the essential characteristics of both synovial membranes and synovial fluid (SF) in PsA. Similarly to other inflammatory arthritis, the histological peculiarities in PsA synovitis are lining hyperplasia, neoangiogenesis, and sublining infiltration by immune cells and inflammatory mediators. Synovial effusions are frequent in PsA patients and SF analysis allows us to determine the pathological process occurring in the joint. Routine examinations help clinicians in defining the inflammatory status and possibly the detection of specific cell subsets. In addition, pathogenic crystals including monosodium urate and calcium pyrophosphate may be found in PsA SF. Conclusions: SF represents a potential substrate to identify the biomarkers that are useful to predict disease progression and response to medications in PsA patients, thus guiding the choice of appropriate and tailored pharmacological treatment.

Clinical Relevance of Oxidative Stress Biomarkers in Human Flavivirus Infections as Predictors of Disease Progression and Severity.

Several Flaviviridae constitute an emerging threat to global health because of their continuing spread and the expansion of vector habitats, largely driven by climate change and intensified global travel. Infections can result in severe neurological or visceral pathologies. The relationship between oxidative stress (OS), an imbalance between generated reactive oxygen species and the antioxidant defences of the host, and flavivirus infection has been repeatedly demonstrated in in vitro and animal studies, but measuring biomarkers of oxidative stress in vivo could prove useful in clinical patient management. We summarise the knowledge and prospects of measuring peripheral OS biomarker levels for clinical case management and correlation with disease severity in six important human flavivirus infections (dengue virus (DENV), Japanese encephalitis virus, West Nile virus (WNV), tick-borne encephalitis virus (TBEV), yellow fever virus and zika virus). We searched the Medline and Web of Science databases for 'Oxidative Stress' AND 'Biomarkers' AND 'Flavivirus', combined with 'clinical', 'in vivo/in vivo', 'patient' and/or 'disease' and included 43 peer-reviewed publications. Correlation between OS and infection has been studied in all six Flaviviridae, but most clinically relevant data are available for DENV, TBEV and WNV. Plasma protein carbonyls, glutathione peroxidase activity and nitrogen monoxide are promising prognostic markers, but their measurement would benefit from methodological harmonisation. Future studies should investigate a broad range of OS biomarkers as predictors of clinically relevant outcomes. We advocate the validation and use of universal or disease-specific oxidative stress indexes that incorporate the most significant outcomes into one, easy-to-use clinical determinant.

Genetic Alterations in Chromatin Regulatory Genes in Upper Tract Urothelial Carcinoma and Urothelial Bladder Cancer.

Upper tract urothelial carcinoma (UTUC) and urothelial carcinoma of the bladder (UCB) share histomorphological and therapeutic features but distinct epidemiologic and clinicopathologic characteristics. We examined alterations of chromatin regulatory genes in molecular subtypes, clonal relatedness, and T-cell receptor (TCR) diversity in UTUC and UCB. Targeted next-generation sequencing or whole-exome DNA sequencing and TCR sequencing were conducted with 34 UTUC and 49 UCB specimens from 63 patients. Tumors were subtyped based on the expression of CK5 and GATA3. Results of tissue microarray of 78 muscle-invasive bladder cancer (MIBC) samples were used as prognostic factors of different subtypes of MIBC. Chromatin regulatory genes were frequently mutated in both UTUC and UCB. Rapid relapse and progression of non-MIBC are correlated with alterations of KMT2C and EP300. Frequency of alterations in chromatin regulatory genes is higher in UTUC patients with SBS22 and SBS2 signatures and lower in UCB patients with SBS2 and SBS6 signatures. GATA3 and CK5 double-positive patients with higher frequencies of SMARCA4, ARID1A, and EP300 mutations have better prognoses than patients with basal subtypes. Although UTUC and UCB in the same patient can be either clonally related or developed independently, mutated genes in chromatin pathway were enriched in the related clones. Compared to UTUC, UCB had more deleterious mutations in DNA damage repair (DDR) genes, higher levels of tumor mutation burden (TMB) and copy number variations (CNVs), as well as higher TCR clonality and lower TCR diversity. Since genetic alterations of the chromatin pathway genes are important in both UTUC and UCB, they could serve as potential biomarkers for predicting disease progression and therapeutic targets. Differences in mutation frequencies of DDR pathway, TMB, CNV, and TCR might be the contributing factors for the distinct responses to immune checkpoint inhibitor (ICI) between UTUC and UCB.

BIG DATA AND CHRONIC DISEASE MANAGEMENT THROUGH PATIENT MONITORING AND TREATMENT WITH DATA ANALYTICS

This systematic review investigates the role of predictive analytics in chronic disease management, focusing on its capacity to predict disease progression, reduce hospitalizations, and enhance patient outcomes. A total of 35 peer-reviewed articles were analyzed to evaluate how predictive models, utilizing data from electronic health records (EHRs), wearable devices, and real-time monitoring systems, are applied in managing chronic diseases such as diabetes and cardiovascular conditions. The findings indicate that predictive models have significantly improved early disease detection accuracy, with studies showing improved forecasting of heart failure exacerbations and notable reductions in hospital readmissions due to timely interventions. Additionally, many studies reported a reduction in healthcare utilization through predictive analytics-driven early interventions. However, challenges regarding data quality and model accuracy were frequently cited, particularly concerning data integration and harmonization across various healthcare systems. Ethical and privacy concerns, including data security and algorithmic bias, were also highlighted, underscoring the need to address these issues for responsible and equitable use of predictive analytics. This review affirms the growing impact of predictive analytics in chronic disease management, while calling for advancements in data management and ethical frameworks to maximize its potential.

Disorders in cytokine synthesis in women with uterine fibroids

In the structure of gynecological diseases, uterine fibroids occupy an “honorable” second place after inflammatory processes of the genital organs, and its share reaches 40%. Uterine fibroids are a benign monoclonal tumor that develops from one abnormal smooth muscle cell of the myometrium, which, as a result of mutation, has the ability to grow unregulated. Common symptoms of the disease include pelvic discomfort or pain, excessive uterine bleeding, secondary anemia, infertility, and bowel and bladder dysfunction. Uterine fibroids can negatively affect the general condition of a woman, causing hormonal, vegetative-vascular and psycho-emotional disorders. Moreover, about 80% of patients with uterine fibroids undergo radical surgical treatment. The mechanisms of development and growth of this benign tumor have not been fully established and remain controversial. The role of immune disorders in the pathogenesis of fibroids is currently being discussed. It has been proven that the growth of fibroids is accompanied by a weakening of immune defense against the background of an increase in the level of proinflammatory cytokines, which are regulators of the processes of proliferation and apoptosis, mediators of the action of sex steroids. The aim of the study was to study the level of some pro-inflammatory cytokines (IL-1β, IL-2, TGF-β2 and MCP-1) in the blood serum of women of reproductive age with uterine fibroids, depending on the size and nature of tumor growth. A comparative analysis of the results of examination of 123 women with uterine fibroids, who made up 2 groups: 1st group of 65 women with simple uterine fibroids and 2nd group of 58 women with rapidly growing uterine fibroids, is presented. The examination included a comprehensive clinical and laboratory study. The level of cytokines (IL-1β, IL- 2, TGF-β2, MCP-1) in the blood serum was assessed by ELISA. It was found that in patients of reproductive age with uterine fibroids, there is an increase in the levels of IL-1β, TGF-β2 and MCP-1. In women with rapidly growing uterine fibroids, these changes are more pronounced. The concentration of IL-2 is reduced in all women with uterine fibroids, regardless of the size and nature of tumor growth. It has been established that an increase in the size of myomatous nodes is accompanied by an increase in immune imbalance: there is an increase in the levels of pro-inflammatory cytokines (IL-1β, TGF-β2, MCP-1) against the background of a reduced content of the lymphokine IL-2, which we regard as possible links in the pathogenesis of uterine fibroids. A pronounced deficiency of IL-2 in the blood serum against the background of a sharp increase in the concentration of TGF-β2 and MCP-1 in the blood of women with uterine fibroids can be considered as a negative criterion for predicting disease progression and used as an additional prognostic marker of tumor growth

Unmasking Neuroendocrine Prostate Cancer with a Machine Learning-Driven Seven-Gene Stemness Signature That Predicts Progression.

Prostate cancer (PCa) poses a significant global health challenge, particularly due to its progression into aggressive forms like neuroendocrine prostate cancer (NEPC). This study developed and validated a stemness-associated gene signature using advanced machine learning techniques, including Random Forest and Lasso regression, applied to large-scale transcriptomic datasets. The resulting seven-gene signature (KMT5C, DPP4, TYMS, CDC25B, IRF5, MEN1, and DNMT3B) was validated across independent cohorts and patient-derived xenograft (PDX) models. This signature demonstrated strong prognostic value for progression-free, disease-free, relapse-free, metastasis-free, and overall survival. Importantly, the signature not only identified specific NEPC subtypes, such as large-cell neuroendocrine carcinoma, which is associated with very poor outcomes, but also predicted a poor prognosis for PCa cases that exhibit this molecular signature, even when they were not histopathologically classified as NEPC. This dual prognostic and classifier capability makes the seven-gene signature a robust tool for personalized medicine, providing a valuable resource for predicting disease progression and guiding treatment strategies in PCa management.

Exploring diagnostic biomarkers of type 2 cardio-renal syndrome based on secreted proteins and bioinformatics analysis

Chronic heart failure (CHF) can induce chronic kidney disease (CKD), called type 2 cardio-renal syndrome (CRS2). The mechanism is not completely clear, and there is a lack of early warning biomarkers of CKD in the context of CHF. Two CKD, one CHF-PBMC and four CHF-cardiac tissue expression profile datasets were obtained from GEO database. Differential expression analysis and WGCNA were used to detect CKD key genes and CHF-related secreted proteins. Protein–protein interaction (PPI), functional enrichment, and cMAP analysis reveal potential mechanisms and drugs of CHF-related CKD. Five machine learning algorithms were used to screen candidate biomarkers, construct a diagnostic nomogram for CKD and validate it in two external cohorts. Clinical serum samples were collected in our hospital to evaluate the correlation and diagnostic value of biomarkers and CKD. 225 CKD key genes and 316 CHF-related secreted proteins were identified. Four key subgroups, including 204 genes, were identified as CRS2-related pathogenic genes by PPI analysis. Enrichment analysis revealed that the identified subgroups exhibited significant enrichment in cytokine action, immune responses, and inflammatory processes. The cMAP analysis highlighted metiradone as a drug with greater potential for therapeutic intervention for CRS2. Utilizing five machine learning algorithms, three hub genes (CD48, COL3A1, LOXL1) were pinpointed as potential biomarkers for CKD, and a nomogram model was constructed. Receiver operating characteristic analysis demonstrated that the nomogram’s area under the curve (AUC) exceeded 0.80 in both the CKD combined dataset and two external cohorts. In addition, the three biomarkers were significantly correlated with the glomerular filtration rate, and the AUC of the model predicting disease progression was 0.944. Furthermore, analysis of immune cell infiltration indicated a correlation between the three biomarkers and the infiltration fraction of macrophages, neutrophils, and other immune cells in CKD. Our clinical cohort validated the expression patterns of three biomarkers in serum, and the diagnostic model achieved an AUC of 0.876. CHF has the potential to facilitate the progression of CKD via the release of cardiac and PBMC secreted proteins. Furthermore, CD48, COL3A1, and LOXL1 have been identified as potential biomarkers for the detection of CKD.

Trajectories of disease evolution upon treatment initiation in systemic lupus erythematosus: results from four clinical trials of belimumab.

Upon commencement of therapy for active disease, patients with systemic lupus erythematosus (SLE) show varying evolution regarding disease activity measures and patient-reported outcomes (PROs). Our objective was to identify disease evolution trajectories to gain a deeper understanding of SLE progression, ultimately improving future trial design. Patients with ≥2 visits and available data on SLEDAI-2K, BILAG, PGA, FACIT-F, and glucocorticoid use were included in a post-hoc analysis of four randomised controlled trials of belimumab (BLISS-52, BLISS-76, BLISS-SC, EMBRACE). Growth mixture modelling identified latent classes. Among 2868 patients analysed, baseline median disease duration was 4.5 (IQR: 1.5-9.7) years and mean (± standard deviation) SDI 0.7 (±2.0), SLEDAI-2K 10.2 (±3.6), BILAG 17.0 (±7.8), PGA 1.5 (±0.5), FACIT-F 30.6 (±11.9), and prednisone dose 11.0 (±8.9) mg/day. In the initial model, glucocorticoid use and dose yielded high standard errors, indicating a weak link with the latent process. A refined model considered only clinical measures and FACIT-F, corrected for intervention and SDI; no other covariates improved the fit. Four classes best described disease evolution: highly active, responders; highly active, non-responders; moderately active, responders; moderately active, non-responders. LLDAS and DORIS remission attainment associated with latent classes. By linking disease activity measures with PROs, we identified four distinct trajectories describing SLE evolution following the initiation of therapy. This classification could be valuable for personalising treatment and guiding biological studies aimed at distinguishing patients with varying anticipated treatment responses, as no single clinical variable alone can predict disease progression.

Rare variants and survival of patients with idiopathic pulmonary fibrosis.

The clinical course of idiopathic pulmonary fibrosis (IPF) is highly variable and unpredictable, with multiple genetic variants influencing IPF outcomes. Notably, rare pathogenic variants in telomere-related genes are associated with poorer clinical outcomes in these patients. Here we assessed whether rare qualifying variants (QVs) in monogenic adult-onset pulmonary fibrosis (PF) genes are associated with IPF survival. Using polygenic risk scores (PRS), we also evaluated the influence of common IPF risk variants in individuals carrying these QVs. We identified QVs in telomere and non-telomere genes linked to monogenic PF forms using whole-genome sequences (WGS) from 888 Pulmonary Fibrosis Foundation Patient Registry (PFFPR) individuals. We also derived a PRS for IPF (PRS-IPF) from 19 previously published common sentinel IPF variants. Using regression models, we then examined the mutual relationships of QVs and PRS-IPF and their association with survival. Validation of results was sought in WGS from an independent IPF study (PROFILE, n=472), and results from the two cohorts were meta-analyzed. Carriers of QVs in monogenic adult-onset PF genes, representing nearly 1 out of 6 IPF patients, were associated with lower PRS-IPF (Odds Ratio [OR]: 1.79; 95% Confidence Interval [CI]: 1.15-2.81; p=0.010) and shorter survival (Hazard Ratio [HR]: 1.53; 95% CI: 1.12-2.10; p=7.3×10 -3 ). Notably, carriers of pathogenic variants at telomere genes showed the strongest association with survival (HR: 1.76; 95% CI: 1.13-2.76; p=0.013). The meta-analysis of the results showed a consistent direction of effect across both cohorts. We revealed the opposite effects of QVs and PRS-IPF on IPF survival. Thus, a distinct IPF molecular subtype might be defined by QVs in monogenic adult-onset PF genes. Assessing the carrier status for QVs and modelling PRS-IPF promises to further contribute to predicting disease progression among IPF patients.

Prognostic Impact of H19/Cell Adhesion Molecules Circuitry on Prostate Cancer Biopsy.

Metastatic prostate cancer (PCa) presents a significant challenge in oncology due to its high mortality rate and the absence of effective biomarkers for predicting patient outcomes. Building on previous research that highlighted the critical role of the long noncoding RNA (lncRNA) H19 and cell adhesion molecules in promoting tumor progression under hypoxia and estrogen stimulation, this study aimed to assess the potential of these components as prognostic biomarkers for PCa at the biopsy stage. This research utilized immunohistochemistry and droplet digital PCR to analyze formalin-fixed paraffin-embedded (FFPE) biopsies, focusing on specific markers within the H19/cell adhesion molecules pathway. A novel multivariate analysis led to a "BioScore", a composite biomarker score to predict disease progression. This score is based on evaluating five key markers: the expression levels of Hypoxia-Inducible Factor 2 Alpha (HIF-2α), endothelial Nitric Oxide Synthase (eNOS), β4 integrin, E-cadherin transcript (CDH1), and lncRNA H19. The criteria for the "BioScore" involve identifying three out of these five markers, combining elevated levels of HIF-2α, eNOS, β4 integrin, and CDH1 with reduced H19 expression. This finding suggests the possibility of identifying, at the time of biopsy, PCa patients at higher risk of metastasis based on dysregulation in the H19/cell adhesion molecules circuitry. This study provides a valuable opportunity for early intervention in managing PCa, potentially contributing to personalized treatment strategies.

18F]NaF PET/CT as a Marker for Fibrodysplasia Ossificans Progressiva: From Molecular Mechanisms to Clinical Applications in Bone Disorders.

Fibrodysplasia ossificans progressiva (FOP) is a rare genetic bone disorder characterized by episodic flare-ups in connective tissue, which are frequently followed by the formation of heterotopic ossification. The absence of available plasma-soluble biomarkers for flare-ups or heterotopic bone formation poses severe challenges to the monitoring of disease activity to measure or predict disease progression. Recently, 18-fluor-sodium fluoride positron emission tomography/computed tomography ([18F]NaF PET/CT) was introduced as a potential marker for ossifying FOP activity. This review discusses the pharmacokinetics of [18F]NaF in relation to the pathophysiology of FOP, and its use as a marker of local bone metabolism in a variety of bone-related disorders. In addition, the review specifically addresses the applicability of [18F]NaF PET/CT imaging in FOP as a monitoring modality.

Exploring serum bile acids as potential noninvasive biomarkers for nonalcoholic fatty liver disease

BackgroundBile acids are vital regulators of liver metabolism, and their dysregulation is closely linked with the progression of nonalcoholic fatty liver disease (NAFLD). Profiling these bile acids may provide valuable diagnostic and prognostic markers for these conditions. This study aimed to evaluate bile acid profiles in NAFLD patients and assess their potential as biomarkers for diagnosing and predicting disease progression. Serum levels of 14 bile acids were measured in 25 normal healthy controls (NHC), 35patients with metabolic dysfunction–associated steatotic liver disease (MASLD), and 40 patients with NASH, categorized by the NAFLD Activity Score (NAS). Quantification was performed using high-performance liquid chromatography-tandem mass spectrometry (HPLC–MS/MS).ResultsPrimary unconjugated bile acids, CA and CDCA, along with conjugated acids GCA, GCDCA, TCA, and TCDCA, were significantly elevated in both MASLD and NASH compared to NHC (all p < 0.05). While levels increased progressively from NHC to MASLD to NASH, no significant differences were observed between MASLD and NASH except for GCA and TCA (P < 0.05). Similarly, secondary bile acids LCA, TLCA, GUDCA, and TUDCA were higher in MASLD and NASH compared to NHC (all p < 0.05).Logistic regression identified CA (odds ratio = 2.05, p = 0.02), CDCA (odds ratio = 1.58, p = 0.04), GCA (odds ratio = 1.92, p = 0.03) and DCA (odds ratio = 2.06, p = 0.04) as significant predictors of fibrosis. For active inflammation, GCA (odds ratio = 2.04, p = 0.04), and TCA (odds ratio = 1.94, p = 0.04) were significant predictors. In steatosis, CA, CDCA, GCA, DCA, TDCA, TLCA, and UDCA were notable predictors, with high odds ratios.ConclusionThe study highlights significant alterations in bile acid profiles associated with NAFLD progression. Specific bile acids, such as CA, GCA, TCA, and TCDCA are strong predictors of disease severity, indicating their potential as biomarkers for NAFLD treatment and prognosis.

A-311 Evaluating the Predictive Value of the sFlt-1/PlGF Ratio in Hypertensive Pregnancies for Preeclampsia

Abstract Background Preeclampsia (PE) is a hypertensive pregnancy disorder affecting around 5% of all pregnancies, and it continues to be a leading cause of maternal and neonatal morbidity and mortality in the United States. The incidence of hypertensive pregnancies is on the rise, attributed to the increasing prevalence of obesity and chronic hypertension. The development of a straightforward diagnostic test to accurately differentiate PE from gestational or chronic hypertension is crucial to avoid unnecessary hospitalizations. Despite the ongoing development of prediction models for PE, conventional clinical and laboratory measures have not been effective in predicting disease progression. Recent research has focused on the ratio of two serum biomarkers: soluble fms-like tyrosine kinase-1 (sFlt-1) and placental growth factor (PlGF), particularly in patients with a clinical suspicion of PE in acute settings. These studies have shown promise, with many indicating that the sFlt-1/PlGF ratio can effectively predict the presence or absence of PE. This study was conducted to assess the predictive value of the sFlt-1/PlGF ratio in pregnancies complicated by hypertension, aiming to contribute to the early detection and management of PE. Methods Pregnant individuals with hypertensive disorders at or above the age of 18 receiving prenatal care at NewYork-Presbysterian-Weill Cornell Hospital were enrolled in this study. We measured the levels of sFlt-1 and PlGF using fully automated Elecsys immunoassays (Roche Diagnostics). Subsequently, a prospective analysis of the serum sFlt-1/PlGF ratio was performed. A receiver operating characteristic (ROC) curve was used to calculate the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the ratio in predicting PE. To assess the prognostic accuracy of the sFlt-1/PlGF ratio, patients were randomly assigned to a development and a validation cohort at a ratio of 30:70. Results We collected a total of 137 serum samples of hypertensive pregnancies, with all ≥20 weeks of gestational age (GA). Of these, 47 (34.3%) developed PE within 2 weeks. The samples were divided into two cohorts: 98 were included in the development cohort, and 39 in the validation cohort. Within these groups, 34 (34.7%) of the development cohort and 13 (33.3%) of the validation cohort developed PE within 2 weeks, respectively. In the development cohort, the median sFlt-1/PlGF ratio was 85 (Interquartile range, [IQR], 44-164) among pregnant women who developed PE within 2 weeks. This was significantly higher compared to a median ratio of 8 (IQR, 3-35) observed in those who did not develop PE (p&amp;lt;0.001). The area under the ROC curve was 0.87, indicating good discriminative ability. An optimized cutoff ratio of 38 was determined, based on the max of combined sensitivity and specificity. When applied into the validation cohort, this cutoff yields a 90% (95% confidence interval [CI], 70-97) NPV and 58% (95%CI, 37-76) PPV with an 85% sensitivity (95%CI, 58-96) and a 69% specificity (95%CI, 50-83). Conclusions In women with a hypertensive disorder of pregnancy presenting GA≥20 weeks of gestation, the measurement of serum sFlt-1/PlGF ratio demonstrated clinical value in predicting the development of PE.